Atmospheric Pattern–Based Predictions of S2S Sea Level Anomalies for Two Selected U.S. Locations

Abstract

Abstract With climate change causing rising sea levels around the globe, multiple recent efforts in the United States have focused on the prediction of various meteorological factors that can lead to periods of anomalously high tides despite seemingly benign atmospheric conditions. As part of these efforts, this research explores monthly scale relationships between sea level variability and atmospheric circulation patterns and demonstrates two options for subseasonal to seasonal (S2S) predictions of anomalous sea levels using these patterns as inputs to artificial neural network (ANN) models. Results on the monthly scale are similar to previous research on the daily scale, with above-average sea levels and an increased risk of high-water events on days with anomalously low atmospheric pressure patterns and wind patterns leading to onshore or downwelling-producing wind stress. Some wind patterns show risks of high-water events to be over 6 times higher than baseline risk and exhibit an average water level anomaly of +94 mm above normal. In terms of forecasting, nonlinear autoregressive ANN models with exogenous input (NARX models) and pattern-based lagged ANN (PLANN) models show skill over postprocessed numerical forecast model output, and simple climatology. Damped-persistence forecasts and PLANN models show nearly the same skill in terms of predicting anomalous sea levels out to 9 months of lead time, with a slight edge to PLANN models, especially with regard to error statistics. This perspective on forecasting—using predefined circulation patterns along with ANN models—should aid in the real-time prediction of coastal flooding events, among other applications.